Machine learning-based identification of core regulatory targets in 1-nitropyrene-induced placental trophoblast dysfunction and FGR.

1-NP (1-Nitropyrene), a nitrated polycyclic aromatic hydrocarbon derived from diesel combustion, is a widespread environmental pollutant with potential reproductive toxicity. However, its role in FGR (fetal growth restriction) and the underlying mechanisms remain unclear. Our in vivo results revealed that 1-NP exposure leads to FGR and placental damage in mice, marked by reduced fetal and placental weights, and altered labyrinthine morphology. Integration of scRNA-seq (single-cell RNA sequencing) and bulk RNA-seq data using the Scissor algorithm identified EVTs (extravillous trophoblasts) as the cell population most strongly associated with the FGR phenotype. We exposed HTR8/SVneo trophoblast cells to low and high concentrations of 1-NP and performed transcriptomic analysis. We intersected transcriptomic profiles with EVT differentiation-associated genes and then applied machine learning to identify candidate hub genes related to EVT function, including CPS1, ERRFI1, GSTA4, and HK2. These genes are also associated with trophoblast functions such as vascular formation and migration, providing insight into the underlying mechanisms by which 1-NP impacts placental morphology. We validated these genes in both 1-NP animal and cellular models, further supporting their potential roles as molecular candidates and targets in 1-NP-induced FGR. Additionally, molecular docking and molecular dynamics analysis provided preliminary evidence for possible binding of 1-NP to these proteins. Together, these findings suggest that 1-NP exposure contributes to placental dysfunction through EVT-associated molecular alterations and highlight a potential link between environmental pollutants and FGR pathogenesis.